Magnetic resonance imaging (MRI) includes techniques for capturing data related to the internal structure of an object of interest, for example, by non-invasively obtaining images of internal structure of the human body. MRI has been widely used as a diagnostic tool in the medical community. MRI exploits the nuclear magnetic resonance (NMR) phenomenon to distinguish different structures, phenomena or characteristics of an object of interest. For example, in biological subjects, MRI may be employed to distinguish between various tissues, organs, anatomical anomalies (e.g., tumors), and/or to image diffusion, blood flow, blood perfusion, etc.
In general, MRI operates by manipulating spin characteristics of subject material. MRI techniques include aligning the spin characteristics of nuclei of the material being imaged using a generally homogeneous magnetic field and perturbing the magnetic field with a sequence of radio frequency (RF) pulses. To invoke the NMR phenomenon, one or more resonant coils may be provided proximate an object positioned within the magnetic field. The RF coils are adapted to generate RF pulses, generally in the form of RF pulse sequences adapted for a particular MRI application, to excite the nuclei and cause the spin to precess about a different axis (e.g., about an axis in the direction of the applied RF pulses). When an RF pulse subsides, spins gradually realign with the magnetic field, releasing energy that can be measured to capture NMR data about the internal structure of the object.
Diffusion-weighted MRI (DW-MRI) is an MR technique sensitive to the incoherent motion of water molecules inside an area of interest. Motion of water molecules is known to be a combination of a slow diffusion component associated with the Brownian motion of water molecules, and a fast diffusion component associated with the bulk motion of intravascular molecules, for example, in the micro-capillaries of tissue vasculature. These phenomena may be characterized via a model referred to herein as the intra-voxel incoherent motion (IVIM) model having a slow diffusion decay parameter (D), a fast diffusion decay parameter (D*), and a fractional contribution (f) of the above two motion components of the DW-MRI signal decay.
IVIM model parameters have shown promise as quantitative imaging biomarkers for various clinical applications in the body including differential analysis of tumors, the assessment of liver cirrhosis, and Crohn's disease. However, conventional implementations of the IVIM model have substantial drawbacks. For example, the IVIM model has conventionally been used to characterize only signal decay related to intra-voxel incoherent motion of water molecules, while both inter-voxel and intra-voxel incoherent motion of water molecules are related to the DW-MRI signal decay.
Moreover, the use of the IVIM model in connection with DW-MRI imaging is conventionally problematic due to the difficulty in determining reliable estimates of the IVIM model parameters (i.e., the fast and slow diffusion decay parameters and the fractional contribution thereof) from the DW-MRI signal/data. In particular, reliable estimates of IVIM model parameters are difficult to obtain for a number of factors including, but not limited to, 1) the non-linearity of the DW-MRI signal decay; 2) the limited number of DW-MRI images as compared to the number of the IVIM model parameters; and 3) the low signal-to-noise ratio (SNR) observed in DW-MRI signals obtained from the body.
Conventional approaches employed to address these difficulties have been generally unsatisfactory such that utilizing the IVIM model has in the past largely been ruled out as a viable solution for clinical settings. FIG. 1A illustrates schematically a conventional approach to determining estimates for the IVIM model parameters. In FIG. 1A (as in the illustrations in FIGS. 1B-1D as well), S represents the signal obtained by performing DW-MRI acquisition and Θ represents IVIM model parameter estimates. The subscripts identify a particular voxel. As such, FIG. 1A shows signal components corresponding to four different voxels being used to independently estimate the IVIM model parameters for the respective voxel. As a result, the approach in FIG. 1A (amongst other problems discussed below) considers only intra-voxel relationships.
Conventionally, a number of approaches have been proposed to address one or more of the above described issues with implementing the IVIM model for DW-MRI data. For example, addressing the non-linearity complication has been attempted by approximating the non-linear DW-MRI signal decay by a log-linear model with the apparent diffusion coefficient (ADC) as the decay rate parameter. However, this simplified model precludes the independent characterization of slow diffusion and fast diffusion components, negatively impacting the ability to characterize and accurately quantify biological phenomena taking place inside the body.
Conventional approaches have attempted to address the relatively low SNR of DW-MRI data in a number of different ways. For example, the SNR may be increased by acquiring multiple DW-MRI images from the patient, averaging the results from the multiple acquisitions and using the averaged DW-MRI signal to estimate IVIM model parameters. FIG. 1B schematically illustrates this approach whereby DW-MRI signals Sn from multiple acquisitions are averaged to estimate the IVIM model parameters Θ for each voxel in the image. However, this requires substantially increased acquisition times, which in clinical settings may be unacceptable from a time and cost perspective. Moreover, such increased acquisition times may be unsuitable for imaging children who generally have difficulty remaining still for long periods of time.
The SNR of DW-MRI may also be increased by averaging the DW-MRI signal over a region of interest (ROI) to generally yield more reliable IVIM parameter estimates. FIG. 1C schematically illustrates this approach whereby DW-MRI signals Sn from a ROI (e.g., a predetermined neighborhood of voxels) are averaged to estimate a single set of IVIM parameter estimates for the ROI. However, the resulting parameter estimates do not adequately reflect heterogeneous environments such as the necrotic and viable parts of tumors, due to averaging the signal over a ROI or neighborhood.
Accordingly, while a number of conventional approaches have been proposed that attempt to address issues with using the IVIM model, such approaches generally remain unsatisfactory in a clinical setting and/or for clinical use, either because the acquisition times are too long, the resulting model parameter estimates are not reliable and/or do not adequately describe or quantify certain tissue structures, characteristics or biological phenomena.